Monitoring a person for indications of a brain injury

ABSTRACT

Embodiments include methods, systems and computer program products for monitoring a person for a traumatic brain injury. Aspects include monitoring a gait of the user with one or more accelerometers embedded in the uniform and analyzing, by a processor, one or more characteristics of the gait of the user. Aspects also include determining whether the one or more characteristics of the gait indicate that the user may have suffered the traumatic brain injury and creating an alert that the user of the helmet may have suffered the traumatic brain injury.

DOMESTIC PRIORITY

This application is a continuation of U.S. application Ser. No.14/664,991; Filed: Mar. 23, 2015; which is related to U.S. applicationSer. No. 14/664,987; Filed Mar. 23, 2015; and U.S. application Ser. No.14/664,989; Filed Mar. 23, 2015; the contents of each of which areherein incorporated by reference in their entirety.

BACKGROUND

The present disclosure relates to monitoring a person for indications ofa brain injury, and more specifically, to methods, systems and computerprogram products for using sensors in a uniform to monitor a person forindications of a brain injury.

Generally speaking, safety is a primary concern for both users ofhelmets and manufacturers of helmets. Helmets are used by individualsthat participate in activities that have risk of head trauma, such asthe area of sports, biking, motorcycling, etc. While helmets havetraditionally been used to provide protection from blunt force trauma tothe head, an increased awareness of concussion causing forces hasmotivated a need for advances in helmet technology to provide increasedprotection against concussions. A concussion is a type of traumaticbrain injury that is caused by a blow to the head that shakes the braininside the skull due to linear or rotational accelerations. Recently,research has linked concussions to a range of health problems, fromdepression to Alzheimer's, along with a range of brain injuries. Unlikesevere traumatic brain injuries, which result in lesions or bleedinginside the brain and are detectable using standard medical imaging, aconcussion is often invisible in brain tissue, and therefore onlydetectable by means of a cognitive change, where that change ismeasurable by changes to brain tissue actions, either neurophysiologicalor through muscle actions caused by the brain and the muscles resultingeffects on the environment, for example, speech sounds.

Currently available helmets use accelerometers to measure the forcesthat the helmet, and therefore the head of the user, experiences. Theseaccelerometers can be used to indicate when a force experienced by ahelmet may be sufficiently large so as to pose a risk of a concussion tothe user. However, currently available helmets are prone to providingfalse positives which can lead to unnecessary downtime for the user ofthe helmet. In addition, a large number of false positives may lead toindividuals disregarding the indications generated and therefore afurther degradation of the effectiveness of the monitoring.

SUMMARY

In accordance with an embodiment, a method for monitoring a user of ahelmet for a traumatic brain injury includes monitoring a gait of theuser with one or more accelerometers embedded in the uniform andanalyzing, by a processor, one or more characteristics of the gait ofthe user. The method also includes determining whether the one or morecharacteristics of the gait indicate that the user may have suffered thetraumatic brain injury and creating an alert that the user of the helmetmay have suffered the traumatic brain injury.

In accordance with another embodiment, a uniform for monitoring a userof for a traumatic brain injury includes a processor and one or moreaccelerometers. The processor is configured to perform a method thatincludes monitoring a gait of the user with one or more accelerometersembedded in the uniform and analyzing one or more characteristics of thegait of the user. The method also includes determining whether the oneor more characteristics of the gait indicate that the user may havesuffered the traumatic brain injury and creating an alert that the userof the helmet may have suffered the traumatic brain injury.

In accordance with a further embodiment, a computer program product formonitoring a user of a helmet for a traumatic brain injury includes anon-transitory storage medium readable by a processing circuit andstoring instructions for execution by the processing circuit forperforming a method. The method includes monitoring a gait of the userwith one or more accelerometers embedded in the uniform and analyzing,by a processor, one or more characteristics of the gait of the user. Themethod also includes determining whether the one or more characteristicsof the gait indicate that the user may have suffered the traumatic braininjury and creating an alert that the user of the helmet may havesuffered the traumatic brain injury.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a block diagram illustrating one example of a processingsystem for practice of the teachings herein;

FIG. 2 is a block diagram illustrating one example of a uniform inaccordance with an exemplary embodiment;

FIG. 3 is a flow diagram of a method for monitoring a user for atraumatic brain injury in accordance with an exemplary embodiment;

FIG. 4 is a flow diagram of another method for monitoring a user for atraumatic brain injury in accordance with an exemplary embodiment;

FIG. 5 is a flow diagram of a method for monitoring a user for atraumatic brain injury with a camera embedded in a helmet in accordancewith an exemplary embodiment;

FIG. 6 is a flow diagram of another method for monitoring a user for atraumatic brain injury with a microphone embedded in a helmet inaccordance with an exemplary embodiment;

FIG. 7 is a flow diagram of a method for monitoring a user for atraumatic brain injury based on a gait of the user in accordance with anexemplary embodiment;

FIG. 8 is a flow diagram of another method for monitoring a user for atraumatic brain injury based on a gait of the user in accordance with anexemplary embodiment; and

FIG. 9 is a flow diagram of a further method for monitoring a user for atraumatic brain injury in accordance with an exemplary embodiment.

DETAILED DESCRIPTION

In accordance with exemplary embodiments of the disclosure, methods,systems and computer program products for using sensors in a helmet, oranother suitable piece of a uniform, to monitor a wearer for indicationsof a brain injury are provided. In exemplary embodiments, the sensorsmay include one or more of accelerometers, gyroscopes, cameras,microphones, or the like. In general, the outputs of the sensors areused to monitor one or more physical characteristics or actions of theuser for signs of a traumatic brain injury, such as a concussion. Inexemplary embodiments, a combination of senor readings may be used todetect a possible traumatic brain injury. In exemplary embodiments, oncea possible traumatic brain injury is detected, a signal indicative ofthe detected traumatic brain injury may be created and transmitted.

Referring to FIG. 1, there is shown an embodiment of a processing system100 for implementing the teachings herein. In this embodiment, thesystem 100 has one or more central processing units (processors) 101 a,101 b, 101 c, etc. (collectively or generically referred to asprocessor(s) 101). In one embodiment, each processor 101 may include areduced instruction set computer (RISC) microprocessor. Processors 101are coupled to system memory 114 and various other components via asystem bus 113. Read only memory (ROM) 102 is coupled to the system bus113 and may include a basic input/output system (BIOS), which controlscertain basic functions of system 100.

FIG. 1 further depicts an input/output (I/O) adapter 107 and a networkadapter 106 coupled to the system bus 113. I/O adapter 107 may be asmall computer system interface (SCSI) adapter that communicates with ahard disk 103 and/or tape storage drive 105 or any other similarcomponent. I/O adapter 107, hard disk 103, and tape storage device 105are collectively referred to herein as mass storage 104. Operatingsystem 120 for execution on the processing system 100 may be stored inmass storage 104. A network adapter 106 interconnects bus 113 with anoutside network 116 enabling data processing system 100 to communicatewith other such systems. A screen (e.g., a display monitor) 115 isconnected to system bus 113 by display adaptor 112, which may include agraphics adapter to improve the performance of graphics intensiveapplications and a video controller. In one embodiment, adapters 107,106, and 112 may be connected to one or more I/O busses that areconnected to system bus 113 via an intermediate bus bridge (not shown).Suitable I/O buses for connecting peripheral devices such as hard diskcontrollers, network adapters, and graphics adapters typically includecommon protocols, such as the Peripheral Component Interconnect (PCI).Additional input/output devices are shown as connected to system bus 113via user interface adapter 108 and display adapter 112. A keyboard 109,mouse 110, and speaker 111 all interconnected to bus 113 via userinterface adapter 108, which may include, for example, a Super I/O chipintegrating multiple device adapters into a single integrated circuit.

Thus, as configured in FIG. 1, the system 100 includes processingcapability in the form of processors 101, storage capability includingsystem memory 114 and mass storage 104, input means such as keyboard 109and mouse 110, and output capability including speaker 111 and display115. In one embodiment, a portion of system memory 114 and mass storage104 collectively store an operating system such as the AIX® operatingsystem from IBM Corporation to coordinate the functions of the variouscomponents shown in FIG. 1.

Referring to FIG. 2, a block diagram illustrating one example of auniform 200 in accordance with an exemplary embodiment is shown. As usedherein a uniform is an outfit worn by individual while participating inan activity. The term uniform may include, but is not intended to belimited to, a helmet. In exemplary embodiments, the uniform 200 includesone or more of the following an accelerometer 202, a camera 204, amicrophone 206, a gyroscope 208, a processor 210, a transceiver 212, apower supply 214 and a memory 216. In exemplary embodiments, the powersupply 214 may be a battery configured to provide power to one or moreof the accelerometer 202, the camera 204, the microphone 206, thegyroscope 208, the processor 210 and the transceiver 212.

The processor 210 is configured to receive an output from one or more ofthe accelerometer 202, the camera 204, the microphone 206 and thegyroscope 208 and to determine if a user of the uniform may havesuffered a traumatic brain injury based on the inputs received. Inexemplary embodiments, the processor 210 may also be used to create abaseline profile of the user based on input form the accelerometer 202,the camera 204, the microphone 206 and the gyroscope 208 during a firsttime period and may store the baseline profile in the memory 216. Theprocessor 210 may compare the readings from the accelerometer 202, thecamera 204, the microphone 206 and the gyroscope 208 during a secondtime period with the stored baseline profile to make determination thatthe user of the uniform may have suffered a traumatic brain injury. Uponmaking a determination that the user of the uniform may have suffered atraumatic brain injury the processor 210 utilizes the transceiver totransmit an alert that the user of the uniform may have suffered atraumatic brain injury.

In exemplary embodiments, the processor 210 may be configured toselectively activate one of the camera 204, the microphone 206 and thegyroscope 208 based on the output received from the accelerometer 202exceeding a threshold level. As will be appreciated by those of ordinaryskill in the art, the number and placement of the various sensors(accelerometer 202, camera 204, microphone 206, and gyroscope 208) willdepend upon the type of the uniform and the metrics of the user to bemonitored. In exemplary embodiments, the user metrics that can bemonitored include, but are not limited to, the gait of the user, thespeech of the user and the eyes of the user.

Referring now to FIG. 3, a flow diagram of a method 300 for monitoring auser for a traumatic brain injury in accordance with an exemplaryembodiment is shown. As shown at block 302, the method 300 includesmonitoring a plurality of sensors in a uniform. In exemplaryembodiments, the plurality of sensors includes an accelerometer and atleast one of a camera, a gyroscope and a microphone. Next, as shown atblock 304, the method 300 includes analyzing an output of the pluralityof sensors for a time period. In exemplary embodiments, analyzing theoutput of the plurality of sensors may include comparing the output ofthe plurality of sensors to one or more stored profiles corresponding tonormal measurements from each of the plurality of sensors or to one ormore thresholds for each of the plurality of sensors. As shown atdecision block 306, the method 300 includes determining if the output ofthe plurality of sensors indicates a user of the uniform may havesuffered a traumatic brain injury. If the output of the plurality ofsensors indicates that the user of the uniform may have suffered atraumatic brain injury, the method 300 proceeds to block 308 andincludes transmitting an alert that the user of the uniform may havesuffered a traumatic brain injury. Otherwise, the method 300 returns toblock 302 and continues to monitor the output of the plurality ofsensors.

Referring now to FIG. 4, a flow diagram of another method 400 formonitoring a user for a traumatic brain injury in accordance with anexemplary embodiment is shown. As shown at block 402, the method 400includes monitoring an acceleration experienced by a helmet. Forexample, the acceleration experienced by a helmet may be measured by oneor more accelerators embedded within the helmet. Next, as shown atdecision block 404, the method 400 includes determining if theacceleration experienced by the helmet exceeds a threshold value. If theacceleration experienced by the helmet does not exceed the thresholdvalue, the method returns to block 402 and continues monitoring theacceleration experienced by the helmet. Otherwise, the method 400proceeds to block 406 and includes activating one or more supplementalsensors. In exemplary embodiments, the one or more supplemental sensorsmay include a camera, a gyroscope and/or a microphone.

The method 400 also includes analyzing an output one or moresupplemental sensors for a time period. In exemplary embodiments,analyzing the output of the supplemental sensors may include comparingthe output of the supplemental sensors to one or more stored profilescorresponding to normal measurements from each of the supplementalsensors or to one or more thresholds for each of the supplementalsensors. As shown at decision block 410, the method 400 includesdetermining if the outputs of one of the one or more supplementalsensors indicate a user of the helmet may have suffered a traumaticbrain injury. If the output of one of the supplemental sensors indicatesthat the user of the helmet may have suffered a traumatic brain injury,the method 400 proceeds to block 412 and includes transmitting an alertthat the user of the uniform may have suffered a traumatic brain injury.Otherwise, the method 400 returns to block 402 and continues to monitorthe acceleration experienced by the helmet.

It has been shown that the dilation of the pupil of an individual's eyescorrelates with a cognitive category of the user and that pupil dilationcan be indicative of a traumatic brain injury. Bleeding inside the skullcaused by head injury can cause Anisocoria, which is an unequal pupilsize. Accordingly, in exemplary embodiments, a helmet is provided thatincludes one or more cameras that are used to monitor the eyes of thewearer of the helmet for signs of a traumatic brain injury. For example,the camera may capture images of the eyes and the processor may analyzethe pupil dilation of the helmet wearer. In some embodiments, thecameras may include a light source that is configured to project a lightonto the eyes of the wearer of the helmet to aid in the evaluation ofthe pupil dilation response.

In exemplary embodiments, the processor may also utilize one or moreenvironmental influences, such as an ambient lighting level, indetermining a cognitive state and a risk category for traumatic braininjury. A plurality of cognitive states and risk categories can becreated by monitoring the wearer of the helmet over time and duringdifferent states, i.e., active, inactive, playing, resting, etc. Thesecognitive states and risk categories can be stored and compared withreal-time data collected by camera to map the user's current state to aknown cognitive state and risk category. Deviations from expectedresults during known states can be used detect when the risk oftraumatic brain injury is increased.

Referring now to FIG. 5, a flow diagram of a method 500 for monitoring auser for a traumatic brain injury with a camera embedded in a helmet inaccordance with an exemplary embodiment is shown. As shown at block 502,the method 500 includes monitoring eyes of a user of the helmet with thecamera embedded in the helmet. Next, as shown at block 504, the method500 includes analyzing one or more characteristics of the eyes of theuser. In exemplary embodiments, the one or more characteristics of theeyes of the user may include, but are not limited to, the pupil size ofthe eyes and any differences in the size of one pupil verses the other.Next, as shown at decision block 506, the method 500 includesdetermining if the one or more characteristics of the eyes indicate thatthe user may have suffered a traumatic brain injury. If the one or morecharacteristics of the eyes indicate that the user may have suffered atraumatic brain injury, the method 500 proceeds to block 508 andtransmits an alert that the user of the helmet may have suffered atraumatic brain injury. Otherwise, the method returns to block 502 andcontinues to monitor the eyes of the user of the helmet.

In exemplary embodiments, the helmet may be configured to transmitimages captured by the camera to a separate system for processing andfurther analysis. Such transmission may be periodic, it may be triggeredby a threshold image analysis by the processor embedded in the helmet,or it may be triggered by a reading from one or more sensors in thehelmet, such as the accelerometer. In exemplary embodiments, the user'shistory of collision or medical concerns may also be used to determine arisk assessment, either by the embedded processor or the separateprocessing system. In addition, the helmet may be configured to providea real-time feed of the user's cognitive state to increase theconfidence level of the need for a particular alert or indication. Inexemplary embodiments, an aggregate indication may be used to summarizean overall state of a group of players. This may also help topotentially identify area of risk in the dynamics of player-playerinteraction, overly aggressive players, playing field conditions, etc.

As mentioned, changes in pupil dilation can indicate a severe traumaticbrain injury, including internal brain bleeding. Analyzing thecharacteristics of the eyes of the user can also include analytics ofpupil dilation that subtle, and may include changes in pupil diameterand asymmetries in pupil dilation that are imperceptible to a humanobserver. These may then be useful in detecting milder traumatic braininjury such as concussion, which is typically invisible in medicalimaging scans, and is measured instead a cognitive change. Furthermore,the use of the camera for pupil tracking can provide measures ofpatterns saccadic eye movements, including microsaccades, for thepurpose of detecting a traumatic brain injury.

It has been shown that an analysis of an individual's speech issufficient to assign the individual to a particular cognitive,psychological, or psychiatric category. Accordingly, in exemplaryembodiments, a helmet is provided that includes one or more microphonesand a processor that are used to monitor the speech of a user of thehelmet for signs of a traumatic brain injury. For example, themicrophones are used to capture the speech of the user of the helmet andthe processor performs analysis on the collected speech. In oneembodiment, the analysis can include a graphical analysis of wordsspoken by the user to determine cognitive state, such as by means of aspeech graph. In another embodiment, the analysis can include ananalysis of the prosody of the user's speech to determine the user'scognitive state and changes thereto. In many cases, a lack of prosody inthe user's speech may be indicative that the user has suffered aconcussion. In other embodiments, the analysis may include the detectionof specific changes in the individual's speech patterns such as syllabledurations and reduced peak velocity/amplitude ratios.

In exemplary embodiments, a plurality of cognitive states and riskcategories can be created by monitoring the speech of the wearer of thehelmet over time and during different states, i.e., active, inactive,playing, resting, etc. These cognitive states and risk categories can bestored and compared with the real-time speech data collected bymicrophone to map the user's current state to a known cognitive stateand risk category. As a result, deviations of the current state from oneof the known states can be used detect when the risk of traumatic braininjury is increased.

In exemplary embodiments, the helmet may also include a vibrationdetection sensor that is disposed close to skull of the wearer that canbe used by the processor to disambiguate speech generated by the wearerof the helmet from the speech of other nearby individuals. In exemplaryembodiments, the processor embedded in the helmet may be configured toperform a speech to text conversion. In other embodiments, the soundcaptured by the microphones may be transmitted to a separate system forprocessing and further analysis.

In exemplary embodiments, the user's history of collision or medicalconcerns may also be used in determining a risk assessment, either bythe embedded processor or the separate processing system. In addition,the helmet may be configured to provide a real-time feed of the speechor the user's cognitive state to increase the confidence level of theneed for a particular alert or indication. In exemplary embodiments, anaggregate indication may be used to summarize an overall state of agroup of players. This may also help to potentially identify area ofrisk in the dynamics of player-player interaction, overly aggressiveplayers, playing field conditions, etc.

Referring now to FIG. 6, a flow diagram of a method 600 for monitoring auser for a traumatic brain injury with a microphone embedded in a helmetin accordance with an exemplary embodiment is shown. As shown at block602, the method 600 includes monitoring the speech of a user of thehelmet with the microphone embedded in the helmet. Next, as shown atblock 604, the method 600 includes analyzing one or more characteristicsof the speech of the user. In exemplary embodiments, the one or morecharacteristics of the speech of the user may include, but are notlimited to, words spoken by the user (via a speech graph), the prosodyof the users speech, or the rate of the users speech (words/min). Next,as shown at decision block 606, the method 600 includes determining ifthe one or more characteristics of the speech of the user indicate thatthe user may have suffered a traumatic brain injury. If the one or morecharacteristics of the speech of the user indicate that the user mayhave suffered a traumatic brain injury, the method 600 proceeds to block608 and transmits an alert that the user of the helmet may have suffereda traumatic brain injury. Otherwise, the method 600 returns to block 602and continues to monitor the speech of the user of the helmet.

For speech traits and other cognitively correlated traits describedherein, comparing a current cognitive trait to those measured previouslymay occur either within an individual, or across individuals. Forexample, the category of speech features that present after concussionmay be common across individuals, even if speech features vary acrossindividuals before concussion. In this way, detection may be simplifiedand not require historical data from the individual, but instead only aset of predetermined features of a concussed cohort of individuals.

It has been shown that an analysis of an individual's gait is sufficientto assign the individual to a particular cognitive category. Inaddition, quantitative gait analysis can be used to identify walkingabnormalities, which can be used as an indicator that an individual mayhave suffered a traumatic brain injury. Accordingly, in exemplaryembodiments, a uniform is provided that includes one or more sensorsthat are used to monitor the gait of a user for signs of a traumaticbrain injury. For example, the uniform may include multipleaccelerometers disposed in different locations of the uniform and aprocessor to analyze the data collected by the accelerometers. In oneembodiment, the uniform may include accelerometers disposed on parts ofthe body to measures posture and stride and accelerometers disposed in ahelmet to monitor gait. In exemplary embodiments, the system creates andstores a baseline profile of the user based on input form theaccelerometers and compares the real-time readings from theaccelerometers to make determination that the user of the uniform mayhave suffered a traumatic brain injury.

Referring now to FIG. 7, a flow diagram of a method 700 for monitoring auser for a traumatic brain injury based on a gait of the user inaccordance with an exemplary embodiment is shown. As shown at block 702,the method 700 includes monitoring a gait of a user with one or moreaccelerometers disposed on the user. Next, as shown at block 704, themethod 700 includes analyzing one or more characteristics of the gait ofthe user. In exemplary embodiments, the one or more characteristics ofthe gait of the user may include, but are not limited to, a duty factorand a forelimb-hindlimb (arm-leg) phase relationship. The duty factor isthe percent of a total cycle which a given foot is on the ground and aforelimb-hindlimb phase is the temporal relationship between the limbpairs. Next, as shown at decision block 706, the method 600 includesdetermining if the one or more characteristics of the gait of the userindicate that the user may have suffered a traumatic brain injury. Ifthe one or more characteristics of the gait of the user indicate thatthe user may have suffered a traumatic brain injury, the method 700proceeds to block 708 and transmits an alert that the user of the helmetmay have suffered a traumatic brain injury. Otherwise, the method 700returns to block 702 and continues to monitor the gait of the user ofthe helmet.

In one embodiment, a system is provided that analyzes acceleration datareceived from one or more accelerometer in a uniform to determine thepostural and movement indicators of a user. In exemplary embodiments,the postural and movement indicators are correlated to one of a group ofplay categories. In general, muscle memory would dictate that anindividual would only experience slight deviations of accelerationduring certain categories of play. Accordingly, the system can determinethe state of play and then compare the expected acceleration data forthat state of play to the observed readings. A brain or somatic injury,for example to the cerebellum, may indicated by when measures falloutside this expected range given a play category. In exemplaryembodiments, the categories, or states of play, can be shared with aseparate analytics processor to determine risk of certain braininjuries, given the category of play.

Referring now to FIG. 8, a flow diagram of a method 800 for monitoring auser for a traumatic brain injury based on a gait of the user inaccordance with an exemplary embodiment is shown. As shown at block 802,the method 800 includes monitoring a gait of a user. In exemplaryembodiments, the gait may be monitored by one or more accelerometersdisposed on the user and/or by video system that monitors the movementof the user. Next, as shown at block 804, the method 800 includesanalyzing the gait of the user to determine the postural and movementindicators of a user. The method 800 also includes determining a stateof play of the user based on the postural and movement indicators of theuser, as shown at block 806. Next, as shown at block 808, the method 800includes comparing the gait of the user with an expected gait for thestate of play. Next, as shown at decision block 810, the method 800includes determining if the comparison indicates that the user may havesuffered a traumatic brain injury. If the comparison indicates that theuser may have suffered a traumatic brain injury, the method 800 proceedsto block 812 and creates an alert that the user of the helmet may havesuffered a traumatic brain injury. Otherwise, the method 800 returns toblock 802 and continues to monitor the gait of the user.

Referring now to FIG. 9, a flow diagram of a method 900 for monitoring auser for a traumatic brain injury in accordance with an exemplaryembodiment is shown. As shown at block 902, the method 900 includesmonitoring a user during a first time period with one or more sensors.Next, as shown at block 904, the method 900 includes creating a baselineuser profile based on an output from the one or more sensors during thefirst time period. The method 900 also includes monitoring the userduring a second time period with the one or more sensors, as shown atblock 906. Next, as shown at block 908, the method 900 includescomparing the output from the one or more sensors during the second timeperiod with the baseline user profile. Next, as shown at decision block910, the method 900 includes determining if the comparison indicatesthat the user may have suffered a traumatic brain injury. If thecomparison indicates that the user may have suffered a traumatic braininjury, the method 900 proceeds to block 812 and creates an alert thatthe user of the helmet may have suffered a traumatic brain injury.Otherwise, the method 900 returns to block 906 and continues to monitorthe user.

Technical effects and benefits include uniforms that are configured tomonitor individuals for signs of a traumatic brain injury based on avariety of factors. In addition, the uniforms can be configured toprovide an indication that a user has suffered a traumatic brain injurywith an increased confidence by monitoring multiple differentcharacteristics of the user. Such uniforms and helmet can be utilized bysoldiers, athletes, and other individuals at risk for traumatic braininjuries.

Analysis of measurements across sensors may make use of Bayes' rule. Theconfidence determining step 910 determines a confidence level indicatinga likelihood of a user to have a traumatic brain injury. For example, aconfidence determining module may determine that a user has had aconcussion. In some embodiments, the confidence determining module usesBayesian inference to compute the conditional probabilityP(concussion|evidence), where evidence includes any measure of the user.P(concussion|evidence) indicates the probability of concussion giventhat evidence is true, meaning it indicates the probability or level ofbrain injury that the user has had, given that the user has producedcertain measures. If the probability is sufficiently high (i.e., above athreshold probability), the confidence determining module determinesthat it is very likely the user has had a concussion.

The conditional probability P(concussion|evidence), which is theprobability of concussion given that evidence is true, may be computedusing the equation (1) according to Bayes rule:

$\begin{matrix}{{P\left( {{concussion}❘{evidence}} \right)} = \frac{{P\left( {{evidence}❘{concussion}} \right)} \times {P({concussion})}}{P({evidence})}} & (1)\end{matrix}$where P(concussion) and P(evidence) are the probabilities of concussionand evidence, respectively, and P(evidence|concussion) is theprobability of evidence given that concussion is true. In someembodiments, P(evidence|concussion) is computed using a probabilisticmodel that relates concussion (i.e., the condition) to evidence (i.e.,the measures), and this model is learned in advance using, e.g., NaiveBayes, or Bayesian Network models. In the absence of any other evidence(i.e., using only the expressions), P(concussion) is a uniformdistribution—there is a certain probability that the user has aconcussion and a certain probability that the user does not have aconcussion before the measurement begins. P(concussion), however, maynot be a uniform distribution if evidence other than the expressions ofthe user is considered before the conversation begins. For instance,P(concussion) may start from a 30% probability that the user isconcussed. In such cases, the number of measures needed forP(concussion|evidence) to exceed the threshold probability may be fewer.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method for monitoring a user of a uniform for atraumatic brain injury, the method comprising: monitoring anacceleration of a head of a user with a plurality of sensors embedded inthe uniform, wherein the uniform comprises a helmet and an outfit wornby the user; wherein the plurality of sensors comprise a gyroscope andan accelerometer; wherein the gyroscope is embedded in the helmet;wherein the accelerometer is embedded in the outfit; selectivelyactivating the gyroscope to monitor one or more characteristics of agait of the user based on the acceleration exceeding a threshold level;measuring, by the accelerometer a stride of the user; determining thatthe one or more characteristics of the gait and the measured stride ofthe user indicate that the user may have suffered the traumatic braininjury, wherein the one or more characteristics of the gait of the usercomprises a duty factor and a forelimb-hindlimb phase relationship; andwherein the determination that the one or more characteristics of thegait indicate that the user may have suffered the traumatic brain injuryis based on a comparison of the one or more characteristics of the gaitof the user to a baseline reading of the one or more characteristics ofthe gait and a baseline measurement of the stride of the user; creatingan alert that the user of the helmet may have suffered the traumaticbrain injury.
 2. The method of claim 1, wherein the analyzing includesdetermining postural and movement indicators of the user.
 3. The methodof claim 1, further comprising transmitting the alert to a separatecomputer system for processing and further analysis.